Let’s face it, data scientists work with software engineers and at the end of the day, they have to deploy software. An effective data scientists should be able to work within a software development team, so (s)he must be able to use the tools of the trade. Our previous post introduced the very basics and now we make a further step.
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Our report on using AI to predict the riskiness of public procurement calls based on their text is available on tenders.guru
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I have bad news; being able to write programs is only a tiny fraction of software development and data science. You have to know a lot of things and this can become very frustrating. You will be bombarded with acronyms and silly-named tools, like IDE, git, version control, CI, etc, etc. At some point, you have to start use the tools of the trade along with coding and here we give you some advice on what to use to become a pro.
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What does it mean to build a language model? How it is related to the Beat Generation’s cut-up technique and can be used for text generation? We’ll shed light on these questions using Python!
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Boolean networks are widely used in computational biology to model gene regulatory networks, and they can be very useful in time-series analysis too. Here, we will use the concept of random Boolean networks to generate interesting images which show some sort of repetitive patterns. Let complexity meet generative art and Python!
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We developed a free course for Manning liveProject. Getting Started with Google Colab Using TensorFlow has got a very descriptive title, so it teaches you the very basics of using Colab with TensorFlow.
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We developed a free course for Manning liveProject. Getting Started with Google Colab Using PyTorch has got a very descriptive title, so it teaches you the very basics of using Colab with PyTorch.
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We’ve been working hard on our newest liveProject, Getting Started with Jupyter Notebook during the last few months and now it is freely available!
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We had the opportunity to present our thoughts on content analysis with similarity graphs at the Graph Data Science conference. Below, you can watch our presentation and other useful materials to get started with similarity graphs.
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We close our series on Graph Theory and Network Science for NLP with an overview of the recent developments in the field. It means deep learning enters the scene – finally.
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